In literature, several works implemented DBN for the purpose of HSIC. For instance, [1]} used DBN for land cover classification by combining spectral-spatial information and made a comparison with some other classification approaches. The usual learning process of DBN involves two steps: one is unsupervised pre-training with unlabeled samples and the second is supervised fine-tuning with the help of labeled samples. However, this training process may result in two problems: first, multiple hidden units may tend to respond similarly [2]} due to co-adaptation [3]} and second is linked with the sparsity and selectivity of activations neurons that are some neurons may always be dead or always responding [4]}. To mitigate these two problems, [5]} introduced a diversified DBN model through regularizing the pre-training and fine-tuning process by imposing a diversity prior to enhancing the DBN's classification accuracy for HSI.
